Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [2]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[3]:
<matplotlib.image.AxesImage at 0x1101ac9b0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [4]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[4]:
<matplotlib.image.AxesImage at 0x110fbfc88>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [5]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
/Users/darkie/anaconda/envs/dl-p3/lib/python3.6/site-packages/ipykernel_launcher.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.
  

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [10]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    inputs_r = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    
    learning_rate = tf.placeholder(tf.float32, name='learing_rate')

    return inputs_r, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [11]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    
    alpha = 0.02
    
    with tf.variable_scope('discriminator', reuse=reuse):
        
        x_1 = tf.layers.conv2d(images, 64, 5, strides=2,
                              kernel_initializer=tf.contrib.layers.xavier_initializer(), padding='same')
        relu_1 = tf.maximum(alpha * x_1, x_1)
        
        x_2 = tf.layers.conv2d(relu_1, 128, 5, strides=2,
                              kernel_initializer=tf.contrib.layers.xavier_initializer(), padding='same')
        bn_2 = tf.layers.batch_normalization(x_2, training=True)
        relu_2 = tf.maximum(alpha * bn_2, bn_2)
    
        
        x_3 = tf.layers.conv2d(relu_2, 256, 5, strides=2,
                              kernel_initializer=tf.contrib.layers.xavier_initializer(), padding='same')
        bn_3 = tf.layers.batch_normalization(x_3, training=True)
        relu_3 = tf.maximum(alpha * bn_3, bn_3)
        
        flat = tf.reshape(relu_3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [12]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    
    alpha = 0.02
    
    with tf.variable_scope('generator', reuse=not is_train):
        x_1 = tf.layers.dense(z, 7*7*512)
        
        x_1 = tf.reshape(x_1, (-1, 7, 7, 512))
        x_1 = tf.layers.batch_normalization(x_1, training=is_train)
        x_1 = tf.maximum(alpha * x_1, x_1)
        
        x_2 = tf.layers.conv2d_transpose(x_1, 256, 5, strides=2,
                                        kernel_initializer=
                                        tf.contrib.layers.xavier_initializer(), padding='same')
        x_2 = tf.layers.batch_normalization(x_2, training=is_train)
        x_2 = tf.maximum(alpha * x_2, x_2)
        
        x_3 = tf.layers.conv2d_transpose(x_2, 128, 5, strides=2,
                                        kernel_initializer=
                                        tf.contrib.layers.xavier_initializer(), padding='same')
        x_3 = tf.layers.batch_normalization(x_3, training=is_train)
        x_3 = tf.maximum(alpha * x_3, x_3)

        # output
        logits = tf.layers.conv2d_transpose(x_3, out_channel_dim, 5, strides=1,
                                            kernel_initializer=
                                            tf.contrib.layers.xavier_initializer(), padding='same')
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [13]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim, is_train=True)
    
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * 0.9))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [15]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    training_variables = tf.trainable_variables()
    
    g_variables = [var for var in training_variables if var.name.startswith('generator')]
    d_variables = [var for var in training_variables if var.name.startswith('discriminator')]

    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_variables)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_variables)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [16]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [20]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    width = data_shape[1]
    height = data_shape[2]
    image_channels = data_shape[3]
    
    input_r, input_z, lr = model_inputs(width, height, image_channels , z_dim)

    d_loss, g_loss = model_loss(input_r, input_z, image_channels)

    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    steps = 0
    print_every = 50
    show_every = 50
    steps_per_epoch = data_shape[0] // batch_size
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                batch_images = batch_images * 2
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={input_r: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_r: batch_images, input_z: batch_z, lr: learning_rate})

                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_r: batch_images, input_z: batch_z, lr: learning_rate})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    # print info
                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Step {}/{}...".format(steps - (epoch_i * steps_per_epoch), steps_per_epoch),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, 25, input_z, image_channels, data_image_mode)
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [21]:
batch_size = 32
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Step 50/1875... Discriminator Loss: 0.5220... Generator Loss: 2.4484
Epoch 1/2... Step 100/1875... Discriminator Loss: 0.4019... Generator Loss: 3.4563
Epoch 1/2... Step 150/1875... Discriminator Loss: 1.0380... Generator Loss: 2.4831
Epoch 1/2... Step 200/1875... Discriminator Loss: 0.9197... Generator Loss: 1.9167
Epoch 1/2... Step 250/1875... Discriminator Loss: 0.9983... Generator Loss: 2.0447
Epoch 1/2... Step 300/1875... Discriminator Loss: 1.1565... Generator Loss: 0.6944
Epoch 1/2... Step 350/1875... Discriminator Loss: 1.0318... Generator Loss: 1.2458
Epoch 1/2... Step 400/1875... Discriminator Loss: 1.7165... Generator Loss: 0.3850
Epoch 1/2... Step 450/1875... Discriminator Loss: 0.9916... Generator Loss: 1.2034
Epoch 1/2... Step 500/1875... Discriminator Loss: 1.2830... Generator Loss: 0.6449
Epoch 1/2... Step 550/1875... Discriminator Loss: 1.1483... Generator Loss: 1.5273
Epoch 1/2... Step 600/1875... Discriminator Loss: 0.9494... Generator Loss: 1.0805
Epoch 1/2... Step 650/1875... Discriminator Loss: 1.1181... Generator Loss: 0.9522
Epoch 1/2... Step 700/1875... Discriminator Loss: 1.0223... Generator Loss: 0.9815
Epoch 1/2... Step 750/1875... Discriminator Loss: 0.9790... Generator Loss: 0.9296
Epoch 1/2... Step 800/1875... Discriminator Loss: 1.7045... Generator Loss: 0.3513
Epoch 1/2... Step 850/1875... Discriminator Loss: 1.1217... Generator Loss: 0.8577
Epoch 1/2... Step 900/1875... Discriminator Loss: 0.9287... Generator Loss: 1.2447
Epoch 1/2... Step 950/1875... Discriminator Loss: 1.0459... Generator Loss: 0.8952
Epoch 1/2... Step 1000/1875... Discriminator Loss: 1.0754... Generator Loss: 0.9655
Epoch 1/2... Step 1050/1875... Discriminator Loss: 1.3859... Generator Loss: 0.5818
Epoch 1/2... Step 1100/1875... Discriminator Loss: 1.7894... Generator Loss: 0.3598
Epoch 1/2... Step 1150/1875... Discriminator Loss: 1.6372... Generator Loss: 0.3973
Epoch 1/2... Step 1200/1875... Discriminator Loss: 0.9607... Generator Loss: 1.6569
Epoch 1/2... Step 1250/1875... Discriminator Loss: 1.0120... Generator Loss: 0.9830
Epoch 1/2... Step 1300/1875... Discriminator Loss: 1.1238... Generator Loss: 0.9255
Epoch 1/2... Step 1350/1875... Discriminator Loss: 1.4158... Generator Loss: 0.5162
Epoch 1/2... Step 1400/1875... Discriminator Loss: 1.1511... Generator Loss: 0.7972
Epoch 1/2... Step 1450/1875... Discriminator Loss: 1.3838... Generator Loss: 0.7115
Epoch 1/2... Step 1500/1875... Discriminator Loss: 1.0828... Generator Loss: 0.7984
Epoch 1/2... Step 1550/1875... Discriminator Loss: 0.9761... Generator Loss: 0.9740
Epoch 1/2... Step 1600/1875... Discriminator Loss: 1.8750... Generator Loss: 0.4700
Epoch 1/2... Step 1650/1875... Discriminator Loss: 0.7746... Generator Loss: 1.3691
Epoch 1/2... Step 1700/1875... Discriminator Loss: 1.0056... Generator Loss: 0.8854
Epoch 1/2... Step 1750/1875... Discriminator Loss: 1.0519... Generator Loss: 0.8954
Epoch 1/2... Step 1800/1875... Discriminator Loss: 0.8168... Generator Loss: 1.4703
Epoch 1/2... Step 1850/1875... Discriminator Loss: 1.2389... Generator Loss: 0.6631
Epoch 2/2... Step 25/1875... Discriminator Loss: 0.9754... Generator Loss: 1.1823
Epoch 2/2... Step 75/1875... Discriminator Loss: 0.8445... Generator Loss: 1.3205
Epoch 2/2... Step 125/1875... Discriminator Loss: 1.3770... Generator Loss: 0.5906
Epoch 2/2... Step 175/1875... Discriminator Loss: 0.9271... Generator Loss: 1.3595
Epoch 2/2... Step 225/1875... Discriminator Loss: 1.0481... Generator Loss: 1.8660
Epoch 2/2... Step 275/1875... Discriminator Loss: 1.7256... Generator Loss: 0.3547
Epoch 2/2... Step 325/1875... Discriminator Loss: 1.1707... Generator Loss: 0.7713
Epoch 2/2... Step 375/1875... Discriminator Loss: 0.9256... Generator Loss: 1.1535
Epoch 2/2... Step 425/1875... Discriminator Loss: 0.5683... Generator Loss: 1.9303
Epoch 2/2... Step 475/1875... Discriminator Loss: 0.8131... Generator Loss: 1.2086
Epoch 2/2... Step 525/1875... Discriminator Loss: 1.2482... Generator Loss: 2.2693
Epoch 2/2... Step 575/1875... Discriminator Loss: 1.5656... Generator Loss: 0.4698
Epoch 2/2... Step 625/1875... Discriminator Loss: 1.3099... Generator Loss: 0.6455
Epoch 2/2... Step 675/1875... Discriminator Loss: 1.1416... Generator Loss: 0.7977
Epoch 2/2... Step 725/1875... Discriminator Loss: 1.1882... Generator Loss: 0.8335
Epoch 2/2... Step 775/1875... Discriminator Loss: 0.8903... Generator Loss: 3.0314
Epoch 2/2... Step 825/1875... Discriminator Loss: 1.1412... Generator Loss: 0.7267
Epoch 2/2... Step 875/1875... Discriminator Loss: 0.7919... Generator Loss: 1.2032
Epoch 2/2... Step 925/1875... Discriminator Loss: 0.8392... Generator Loss: 1.1739
Epoch 2/2... Step 975/1875... Discriminator Loss: 0.6479... Generator Loss: 2.0830
Epoch 2/2... Step 1025/1875... Discriminator Loss: 0.7820... Generator Loss: 1.1575
Epoch 2/2... Step 1075/1875... Discriminator Loss: 0.6152... Generator Loss: 2.0308
Epoch 2/2... Step 1125/1875... Discriminator Loss: 0.6020... Generator Loss: 1.7574
Epoch 2/2... Step 1175/1875... Discriminator Loss: 1.2896... Generator Loss: 0.7482
Epoch 2/2... Step 1225/1875... Discriminator Loss: 0.9062... Generator Loss: 1.1129
Epoch 2/2... Step 1275/1875... Discriminator Loss: 0.7511... Generator Loss: 1.6398
Epoch 2/2... Step 1325/1875... Discriminator Loss: 0.8376... Generator Loss: 1.2974
Epoch 2/2... Step 1375/1875... Discriminator Loss: 0.7339... Generator Loss: 1.3968
Epoch 2/2... Step 1425/1875... Discriminator Loss: 1.4543... Generator Loss: 0.5791
Epoch 2/2... Step 1475/1875... Discriminator Loss: 1.1304... Generator Loss: 2.1502
Epoch 2/2... Step 1525/1875... Discriminator Loss: 0.9268... Generator Loss: 1.0304
Epoch 2/2... Step 1575/1875... Discriminator Loss: 1.6502... Generator Loss: 0.4058
Epoch 2/2... Step 1625/1875... Discriminator Loss: 1.1673... Generator Loss: 0.7974
Epoch 2/2... Step 1675/1875... Discriminator Loss: 0.7451... Generator Loss: 2.2293
Epoch 2/2... Step 1725/1875... Discriminator Loss: 0.7567... Generator Loss: 1.3119
Epoch 2/2... Step 1775/1875... Discriminator Loss: 0.7299... Generator Loss: 1.4060
Epoch 2/2... Step 1825/1875... Discriminator Loss: 1.7997... Generator Loss: 0.3558
Epoch 2/2... Step 1875/1875... Discriminator Loss: 0.6437... Generator Loss: 1.6315

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [22]:
batch_size = 32
z_dim = 100
learning_rate = 0.0003
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Step 50/6331... Discriminator Loss: 0.4366... Generator Loss: 2.7354
Epoch 1/1... Step 100/6331... Discriminator Loss: 0.7610... Generator Loss: 4.5524
Epoch 1/1... Step 150/6331... Discriminator Loss: 0.7944... Generator Loss: 1.3658
Epoch 1/1... Step 200/6331... Discriminator Loss: 0.6644... Generator Loss: 1.9926
Epoch 1/1... Step 250/6331... Discriminator Loss: 1.0711... Generator Loss: 0.9725
Epoch 1/1... Step 300/6331... Discriminator Loss: 1.4295... Generator Loss: 0.5148
Epoch 1/1... Step 350/6331... Discriminator Loss: 1.5629... Generator Loss: 0.4846
Epoch 1/1... Step 400/6331... Discriminator Loss: 1.4076... Generator Loss: 0.5402
Epoch 1/1... Step 450/6331... Discriminator Loss: 1.7296... Generator Loss: 0.3717
Epoch 1/1... Step 500/6331... Discriminator Loss: 1.1744... Generator Loss: 0.7125
Epoch 1/1... Step 550/6331... Discriminator Loss: 1.0348... Generator Loss: 1.1600
Epoch 1/1... Step 600/6331... Discriminator Loss: 1.0613... Generator Loss: 1.8044
Epoch 1/1... Step 650/6331... Discriminator Loss: 0.5620... Generator Loss: 2.1247
Epoch 1/1... Step 700/6331... Discriminator Loss: 0.8878... Generator Loss: 1.9904
Epoch 1/1... Step 750/6331... Discriminator Loss: 0.7078... Generator Loss: 2.7720
Epoch 1/1... Step 800/6331... Discriminator Loss: 0.8014... Generator Loss: 1.1885
Epoch 1/1... Step 850/6331... Discriminator Loss: 1.3043... Generator Loss: 0.5905
Epoch 1/1... Step 900/6331... Discriminator Loss: 1.6724... Generator Loss: 0.4004
Epoch 1/1... Step 950/6331... Discriminator Loss: 1.0562... Generator Loss: 0.8654
Epoch 1/1... Step 1000/6331... Discriminator Loss: 1.1701... Generator Loss: 0.7857
Epoch 1/1... Step 1050/6331... Discriminator Loss: 0.9256... Generator Loss: 1.1923
Epoch 1/1... Step 1100/6331... Discriminator Loss: 1.0925... Generator Loss: 0.8057
Epoch 1/1... Step 1150/6331... Discriminator Loss: 1.2667... Generator Loss: 0.8411
Epoch 1/1... Step 1200/6331... Discriminator Loss: 1.2000... Generator Loss: 0.6160
Epoch 1/1... Step 1250/6331... Discriminator Loss: 1.4731... Generator Loss: 0.6746
Epoch 1/1... Step 1300/6331... Discriminator Loss: 0.6864... Generator Loss: 1.4421
Epoch 1/1... Step 1350/6331... Discriminator Loss: 1.6856... Generator Loss: 0.4198
Epoch 1/1... Step 1400/6331... Discriminator Loss: 1.0488... Generator Loss: 1.0566
Epoch 1/1... Step 1450/6331... Discriminator Loss: 1.5382... Generator Loss: 0.5197
Epoch 1/1... Step 1500/6331... Discriminator Loss: 1.1562... Generator Loss: 0.8219
Epoch 1/1... Step 1550/6331... Discriminator Loss: 1.3086... Generator Loss: 0.9945
Epoch 1/1... Step 1600/6331... Discriminator Loss: 1.1779... Generator Loss: 0.8044
Epoch 1/1... Step 1650/6331... Discriminator Loss: 1.0722... Generator Loss: 0.7790
Epoch 1/1... Step 1700/6331... Discriminator Loss: 1.3526... Generator Loss: 0.7046
Epoch 1/1... Step 1750/6331... Discriminator Loss: 0.9651... Generator Loss: 1.1287
Epoch 1/1... Step 1800/6331... Discriminator Loss: 1.5215... Generator Loss: 0.5254
Epoch 1/1... Step 1850/6331... Discriminator Loss: 1.2471... Generator Loss: 0.6825
Epoch 1/1... Step 1900/6331... Discriminator Loss: 1.3521... Generator Loss: 0.6151
Epoch 1/1... Step 1950/6331... Discriminator Loss: 1.4483... Generator Loss: 0.7887
Epoch 1/1... Step 2000/6331... Discriminator Loss: 1.0899... Generator Loss: 1.1579
Epoch 1/1... Step 2050/6331... Discriminator Loss: 1.3010... Generator Loss: 0.7417
Epoch 1/1... Step 2100/6331... Discriminator Loss: 1.1682... Generator Loss: 1.2543
Epoch 1/1... Step 2150/6331... Discriminator Loss: 1.1308... Generator Loss: 0.9807
Epoch 1/1... Step 2200/6331... Discriminator Loss: 1.5785... Generator Loss: 0.4696
Epoch 1/1... Step 2250/6331... Discriminator Loss: 1.5462... Generator Loss: 0.5056
Epoch 1/1... Step 2300/6331... Discriminator Loss: 1.1639... Generator Loss: 0.8170
Epoch 1/1... Step 2350/6331... Discriminator Loss: 1.3021... Generator Loss: 0.7013
Epoch 1/1... Step 2400/6331... Discriminator Loss: 1.3556... Generator Loss: 0.6471
Epoch 1/1... Step 2450/6331... Discriminator Loss: 1.2488... Generator Loss: 0.6573
Epoch 1/1... Step 2500/6331... Discriminator Loss: 1.2079... Generator Loss: 0.7472
Epoch 1/1... Step 2550/6331... Discriminator Loss: 1.2396... Generator Loss: 0.7223
Epoch 1/1... Step 2600/6331... Discriminator Loss: 1.3414... Generator Loss: 0.5779
Epoch 1/1... Step 2650/6331... Discriminator Loss: 1.3269... Generator Loss: 0.5860
Epoch 1/1... Step 2700/6331... Discriminator Loss: 1.1481... Generator Loss: 0.8544
Epoch 1/1... Step 2750/6331... Discriminator Loss: 1.1971... Generator Loss: 0.8095
Epoch 1/1... Step 2800/6331... Discriminator Loss: 1.4734... Generator Loss: 0.5161
Epoch 1/1... Step 2850/6331... Discriminator Loss: 1.5201... Generator Loss: 0.4740
Epoch 1/1... Step 2900/6331... Discriminator Loss: 1.1842... Generator Loss: 0.7798
Epoch 1/1... Step 2950/6331... Discriminator Loss: 1.2173... Generator Loss: 0.7736
Epoch 1/1... Step 3000/6331... Discriminator Loss: 1.2843... Generator Loss: 0.6441
Epoch 1/1... Step 3050/6331... Discriminator Loss: 1.3149... Generator Loss: 0.7598
Epoch 1/1... Step 3100/6331... Discriminator Loss: 1.3143... Generator Loss: 0.6295
Epoch 1/1... Step 3150/6331... Discriminator Loss: 1.6095... Generator Loss: 0.4876
Epoch 1/1... Step 3200/6331... Discriminator Loss: 1.4771... Generator Loss: 0.5963
Epoch 1/1... Step 3250/6331... Discriminator Loss: 1.5516... Generator Loss: 0.5753
Epoch 1/1... Step 3300/6331... Discriminator Loss: 1.6315... Generator Loss: 0.4196
Epoch 1/1... Step 3350/6331... Discriminator Loss: 1.4295... Generator Loss: 0.4657
Epoch 1/1... Step 3400/6331... Discriminator Loss: 1.3863... Generator Loss: 0.6721
Epoch 1/1... Step 3450/6331... Discriminator Loss: 1.4587... Generator Loss: 0.5982
Epoch 1/1... Step 3500/6331... Discriminator Loss: 1.4637... Generator Loss: 0.6238
Epoch 1/1... Step 3550/6331... Discriminator Loss: 1.3404... Generator Loss: 0.6350
Epoch 1/1... Step 3600/6331... Discriminator Loss: 1.4826... Generator Loss: 0.5414
Epoch 1/1... Step 3650/6331... Discriminator Loss: 1.2565... Generator Loss: 0.7710
Epoch 1/1... Step 3700/6331... Discriminator Loss: 1.4297... Generator Loss: 0.6356
Epoch 1/1... Step 3750/6331... Discriminator Loss: 1.3987... Generator Loss: 0.6274
Epoch 1/1... Step 3800/6331... Discriminator Loss: 1.0413... Generator Loss: 1.0184
Epoch 1/1... Step 3850/6331... Discriminator Loss: 1.0065... Generator Loss: 0.9908
Epoch 1/1... Step 3900/6331... Discriminator Loss: 1.3557... Generator Loss: 0.6493
Epoch 1/1... Step 3950/6331... Discriminator Loss: 1.2954... Generator Loss: 0.6585
Epoch 1/1... Step 4000/6331... Discriminator Loss: 1.4111... Generator Loss: 0.5789
Epoch 1/1... Step 4050/6331... Discriminator Loss: 1.3805... Generator Loss: 0.6628
Epoch 1/1... Step 4100/6331... Discriminator Loss: 1.1945... Generator Loss: 0.8069
Epoch 1/1... Step 4150/6331... Discriminator Loss: 1.4220... Generator Loss: 0.5019
Epoch 1/1... Step 4200/6331... Discriminator Loss: 1.1263... Generator Loss: 0.7018
Epoch 1/1... Step 4250/6331... Discriminator Loss: 1.0723... Generator Loss: 0.9329
Epoch 1/1... Step 4300/6331... Discriminator Loss: 1.3463... Generator Loss: 0.5800
Epoch 1/1... Step 4350/6331... Discriminator Loss: 1.3979... Generator Loss: 0.5708
Epoch 1/1... Step 4400/6331... Discriminator Loss: 1.4163... Generator Loss: 0.6041
Epoch 1/1... Step 4450/6331... Discriminator Loss: 1.4096... Generator Loss: 0.6853
Epoch 1/1... Step 4500/6331... Discriminator Loss: 1.2093... Generator Loss: 0.7502
Epoch 1/1... Step 4550/6331... Discriminator Loss: 1.1938... Generator Loss: 0.7478
Epoch 1/1... Step 4600/6331... Discriminator Loss: 1.2765... Generator Loss: 0.6758
Epoch 1/1... Step 4650/6331... Discriminator Loss: 1.3894... Generator Loss: 0.6263
Epoch 1/1... Step 4700/6331... Discriminator Loss: 1.2963... Generator Loss: 0.6147
Epoch 1/1... Step 4750/6331... Discriminator Loss: 1.5123... Generator Loss: 0.5254
Epoch 1/1... Step 4800/6331... Discriminator Loss: 1.6666... Generator Loss: 0.4312
Epoch 1/1... Step 4850/6331... Discriminator Loss: 1.4152... Generator Loss: 0.5051
Epoch 1/1... Step 4900/6331... Discriminator Loss: 1.7197... Generator Loss: 0.4162
Epoch 1/1... Step 4950/6331... Discriminator Loss: 1.4475... Generator Loss: 0.5666
Epoch 1/1... Step 5000/6331... Discriminator Loss: 1.6580... Generator Loss: 0.5001
Epoch 1/1... Step 5050/6331... Discriminator Loss: 1.4745... Generator Loss: 0.5627
Epoch 1/1... Step 5100/6331... Discriminator Loss: 1.3655... Generator Loss: 0.5828
Epoch 1/1... Step 5150/6331... Discriminator Loss: 1.3432... Generator Loss: 0.6832
Epoch 1/1... Step 5200/6331... Discriminator Loss: 1.4328... Generator Loss: 0.5575
Epoch 1/1... Step 5250/6331... Discriminator Loss: 1.1684... Generator Loss: 0.7976
Epoch 1/1... Step 5300/6331... Discriminator Loss: 1.2978... Generator Loss: 0.7850
Epoch 1/1... Step 5350/6331... Discriminator Loss: 1.1187... Generator Loss: 0.8646
Epoch 1/1... Step 5400/6331... Discriminator Loss: 1.5158... Generator Loss: 0.4739
Epoch 1/1... Step 5450/6331... Discriminator Loss: 1.2982... Generator Loss: 0.6290
Epoch 1/1... Step 5500/6331... Discriminator Loss: 1.4025... Generator Loss: 0.5931
Epoch 1/1... Step 5550/6331... Discriminator Loss: 1.3720... Generator Loss: 0.6152
Epoch 1/1... Step 5600/6331... Discriminator Loss: 1.2816... Generator Loss: 0.6890
Epoch 1/1... Step 5650/6331... Discriminator Loss: 1.4466... Generator Loss: 0.5723
Epoch 1/1... Step 5700/6331... Discriminator Loss: 1.2779... Generator Loss: 0.6852
Epoch 1/1... Step 5750/6331... Discriminator Loss: 1.4474... Generator Loss: 0.5578
Epoch 1/1... Step 5800/6331... Discriminator Loss: 1.5938... Generator Loss: 0.4435
Epoch 1/1... Step 5850/6331... Discriminator Loss: 1.2520... Generator Loss: 0.6881
Epoch 1/1... Step 5900/6331... Discriminator Loss: 1.3085... Generator Loss: 0.6960
Epoch 1/1... Step 5950/6331... Discriminator Loss: 1.5894... Generator Loss: 0.4590
Epoch 1/1... Step 6000/6331... Discriminator Loss: 1.4931... Generator Loss: 0.5145
Epoch 1/1... Step 6050/6331... Discriminator Loss: 1.4218... Generator Loss: 0.6984
Epoch 1/1... Step 6100/6331... Discriminator Loss: 1.2910... Generator Loss: 0.6716
Epoch 1/1... Step 6150/6331... Discriminator Loss: 1.3176... Generator Loss: 0.7032
Epoch 1/1... Step 6200/6331... Discriminator Loss: 1.5576... Generator Loss: 0.4412
Epoch 1/1... Step 6250/6331... Discriminator Loss: 1.6540... Generator Loss: 0.4859
Epoch 1/1... Step 6300/6331... Discriminator Loss: 1.6122... Generator Loss: 0.4688

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.